Determination of a desired repository for retrieving search results

ABSTRACT

A system receives a search query from a user and searches a group of repositories, based on the search query, to identify, for each of the repositories, a set of search results. The system also identifies one of the repositories based on a likelihood that the user desires information from the identified repository and presents the set of search results associated with the identified repository.

RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.13/608,991, filed Sep. 10, 2012(now U.S. Pat. No. 9,092,488), which is acontinuation of 12/510,693, filed Jul. 28, 2009(now U.S. Pat. No.8,266,133), which is a continuation of U.S. application Ser. No.11/169,285, filed Jun. 29, 2005 (now U.S. Pat. No. 7,584,177), thedisclosures of which are incorporated herein by reference.

BACKGROUND OF THE INVENTION

Field of the Invention

Implementations described herein relate generally to informationretrieval and, more particularly, to the determination of a desiredrepository for a search.

Description of Related Art

The World Wide Web (“web”) contains a vast amount of information.Locating a desired portion of the information, however, can bechallenging. This problem is compounded because the amount ofinformation on the web and the number of new users inexperienced at websearching are growing rapidly.

Search engine systems attempt to return hyperlinks to web pages in whicha user is interested. Generally, search engine systems base theirdetermination of the user's interest on search terms (called a searchquery) entered by the user. The goal of a search engine system is toprovide links to high quality, relevant search results (e.g., web pages)to the user based on the search query. Typically, the search enginesystem accomplishes this by matching the terms in the search query to acorpus of pre-stored web pages. Web pages that contain the user's searchterms are “hits” and are returned to the user as links.

Some search engine systems can provide various types of information asthe search results. For example, a search engine system might be capableof providing search results relating to web pages, news articles,images, merchant products, usenet pages, yellow page entries, scannedbooks, and/or other types of information. Typically, a search enginesystem provides separate interfaces to these different types ofinformation.

When a user provides a search query to a standard search engine system,the user is typically provided with links to web pages. If the userdesires another type of information (e.g., images or news articles), theuser typically needs to access a separate interface provided by thesearch engine system.

SUMMARY OF THE INVENTION

According to one aspect, a method may include receiving a search queryfrom a user; searching a group of repositories, based on the searchquery, to identify, for each of the repositories, a set of searchresults; identifying one of the repositories based on a likelihood thatthe user desires information from the identified repository; andpresenting the set of search results associated with the identifiedrepository.

According to another aspect, a system may include a search engine systemthat may receive a search query from a user and determine a score foreach of a group of repositories, where the score for one of therepositories is based on a likelihood that the user desires informationfrom the one repository. The search engine system may also perform asearch on one or more of the repositories, based on the search query, toidentify, for each of the one or more repositories, a set of searchresults, and provide one or more of the sets of search results based onthe scores.

According to yet another aspect, a computer-readable medium to storedata and computer-executable instructions is provided. Thecomputer-readable medium may include log data associated with a numberof searches of repositories based on search queries provided by users.The computer-readable medium may also include instructions forrepresenting the log data as triples of data (u, q, r), where u refersto information regarding a user that provided a search query, q refersto information regarding the search query, and r refers to informationregarding a repository from which search results were provided inresponse to the search query; instructions for determining a label foreach of the triples of data (u, q, r), where the label includesinformation regarding whether the user u desired information from therepository r when the user provided the search query q; and instructionsfor training a model based on the triples of data (u, q, r) and theassociated labels, where the model predicts whether a particular userdesires information from a repository when the user provides aparticular search query.

According to a further aspect, a system may include a first repositoryto store a first type of data, a second repository to store a secondtype of data, and a search engine system. The search engine system mayreceive a search query from a user, and determine a likelihood that theuser desires information from the first or second repository based oninformation regarding the user, the search query, and the first orsecond repository.

According to another aspect, a system may include a model generationsystem and a search engine system. The model generation system maygenerate a model that determines a score associated with a likelihoodthat a particular user desires information from a repository when theuser provides a particular search query. The search engine system mayreceive a search query from a user, determine a score for each of aplurality of repositories based on the model, and present search resultsfrom one or more of the repositories based on the scores.

According to yet another aspect, a method may include receiving a searchquery from a user; determining a score for each of a plurality ofrepositories, the score for one of the repositories being based on alikelihood that the user desires information from the one repository;performing a search on at least one of the repositories, based on thesearch query and the determined scores, to identify, for each of the atleast one of the repositories, a set of search results; and providingone or more of the sets of search results.

According to a further aspect, a system may include a model generationsystem to generate first and second models, where at least one factorused to generate the second model is different or absent when generatingthe first model. The system may also include a search engine system toreceive a search query from a user, determine a first score for each ofa plurality of repositories based on the first model, perform a searchon one or more of the repositories based on the search query and thefirst scores, determine a second score for each of the one or more ofthe repositories based on the second model, and present search resultsfrom at least one of the one or more of the repositories based on thesecond scores.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate an embodiment of the inventionand, together with the description, explain the invention. In thedrawings,

FIG. 1 illustrates a concept consistent with principles of theinvention;

FIG. 2 is a diagram of an exemplary model generation system according toan implementation consistent with the principles of the invention;

FIG. 3 is an exemplary diagram of a device of FIG. 2 according to animplementation consistent with the principles of the invention;

FIG. 4 is a flowchart of exemplary processing for generating a modelaccording to an implementation consistent with the principles of theinvention;

FIG. 5 is a diagram of an exemplary information retrieval network inwhich systems and methods consistent with the principles of theinvention may be implemented;

FIG. 6 is a flowchart of exemplary processing for providing searchresults according to an implementation consistent with the principles ofthe invention; and

FIGS. 7-10 are diagrams of exemplary implementations consistent with theprinciples of the invention.

DETAILED DESCRIPTION

The following detailed description of the invention refers to theaccompanying drawings. The same reference numbers in different drawingsmay identify the same or similar elements. Also, the following detaileddescription does not limit the invention.

Overview

FIG. 1 illustrates a concept consistent with principles of theinvention. A search engine system may maintain different types ofinformation that might be desired by a user. The search engine systemmay maintain a set of repositories relating to the different types ofinformation. As shown in FIG. 1, the search engine system may beassociated with, for example, repositories relating to web pages,images, products, and news. The web page repository may includeinformation relating to web pages. The image repository may includeinformation relating to images. The product repository may includeinformation relating to merchant products. The news repository mayinclude information relating to news documents. The search engine systemmay provide separate interfaces for searches directed to specific onesof the repositories.

In the description to follow, the term “document” is to be broadlyinterpreted to include any machine-readable and machine-storable workproduct. A document may include, for example, a web page, informationrelating to a news event, an image file, information relating to amerchant product, information relating to a usenet page, a yellow pageentry, a scanned book, a file, a combination of files, one or more fileswith embedded links to other files, a blog, a web advertisement, ane-mail, etc. Documents often include textual information and may includeembedded information (such as meta information, hyperlinks, etc.) and/orembedded instructions (such as Javascript, etc.). A “link,” as the termis used herein, is to be broadly interpreted to include any referenceto/from a document from/to another document or another part of the samedocument.

As shown in FIG. 1, a user may provide a search query to the searchengine system. The search engine system may determine which repositoryor repositories the user likely desires. The search engine may perform asearch and present search results that include information from one ormore of the repositories based on the determination of which repositoryor repositories the user likely desires.

For example, if a user provides the term “sunset” as a search query tothe search engine system, the search engine system may determine thatthe user is more interested in images of sunsets rather than web pagesrelating to sunsets. As a result, the search engine system may presentthe user with search results from the image repository instead of, or inaddition to, search results from other repositories.

Similarly, if a user provides the phrase “iraq war” as a search query tothe search engine system, the search engine system may determine thatthe user is more interested in news documents relating to the Iraq warrather than web pages relating to the Iraq war. As a result, the searchengine system may present the user with search results from the newsrepository instead of, or in addition to, search results from otherrepositories.

Implementations consistent with the principles of the invention maygenerate a model that predicts which repository, or repositories, a useris interested in when the user provides a search query, and use thismodel to provide relevant search results to the user.

Exemplary Model Generation System

FIG. 2 is an exemplary diagram of a model generation system 200consistent with the principles of the invention. System 200 may includeone or more devices 210 and a store of log data 220. Store 220 mayinclude one or more logical or physical memory devices that may store alarge data set (e.g., millions of instances and hundreds of thousands offeatures) that may be used, as described in more detail below, to createand train a model. The data may include log data concerning priorsearches, such as user information, query information, and repositoryinformation, that may be used to create a model that may be used toidentify one or more repositories that may be desired by a user. In oneimplementation, the model may predict whether a user desires informationfrom a particular repository when the user provides a certain query.

The user information may include Internet Protocol (IP) addresses,cookie information, languages, and/or geographical informationassociated with the users, prior queries provided by the users, and/orthe time of day and/or day of the week that the users provided thecurrent or prior queries. The query information may include informationrelating to the query terms that were provided. The repositoryinformation may include information relating to the repositoryinterfaces used for the searches, the documents that were displayed andthe repositories from which they were obtained, and/or the documentsthat were selected (e.g., clicked on). In other exemplaryimplementations, other types of data may alternatively or additionallybe maintained by store 220.

Device(s) 210 may include any type of computing device capable ofaccessing store 220 via any type of connection mechanism. According toone implementation consistent with the principles of the invention,system 200 may include multiple devices 210. According to anotherimplementation, system 200 may include a single device 210.

FIG. 3 is an exemplary diagram of a device 210 according to animplementation consistent with the principles of the invention. Device210 may include a bus 310, a processor 320, a main memory 330, a readonly memory (ROM) 340, a storage device 350, an input device 360, anoutput device 370, and a communication interface 380. Bus 310 mayinclude a path that permits communication among the elements of device210.

Processor 320 may include a processor, microprocessor, or processinglogic that may interpret and execute instructions. Main memory 330 mayinclude a random access memory (RAM) or another type of dynamic storagedevice that may store information and instructions for execution byprocessor 320. ROM 340 may include a ROM device or another type ofstatic storage device that may store static information and instructionsfor use by processor 320. Storage device 350 may include a magneticand/or optical recording medium and its corresponding drive.

Input device 360 may include a mechanism that permits an operator toinput information to device 210, such as a keyboard, a mouse, a pen,voice recognition and/or biometric mechanisms, etc. Output device 370may include a mechanism that outputs information to the operator,including a display, a printer, a speaker, etc. Communication interface380 may include any transceiver-like mechanism that enables device 210to communicate with other devices and/or systems. For example,communication interface 380 may include mechanisms for communicatingwith another device 210 or store 220.

As will be described in detail below, device 210, consistent with theprinciples of the invention, may perform certain modelgenerating-related operations. Device 210 may perform these operationsin response to processor 320 executing software instructions containedin a computer-readable medium, such as memory 330. A computer-readablemedium may be defined as a physical or logical memory device and/orcarrier wave.

The software instructions may be read into memory 330 from anothercomputer-readable medium, such as data storage device 350, or fromanother device via communication interface 380. The softwareinstructions contained in memory 330 may cause processor 320 to performprocesses that will be described later. Alternatively, hardwiredcircuitry may be used in place of or in combination with softwareinstructions to implement processes consistent with the principles ofthe invention. Thus, implementations consistent with the principles ofthe invention are not limited to any specific combination of hardwarecircuitry and software.

Exemplary Model Generation Processing

For purposes of the discussion to follow, the set of data in store 220(FIG. 2) may include multiple elements, called instances. It may bepossible for store 220 to include millions of instances. Each instancemay include a triple of data: (u, q, r), where “u” refers to userinformation, “q” refers to the query that user u provided, and “r”refers to the repository from which search results were provided inresponse to query q. Store 220 may also store information regardingwhether user u desired information from repository r when user uprovided query q, where the user's desire may be measured, for example,by determining whether the user selected a document from the repository.This information will be referred to as the “label” for the instance.

Several features may be extracted for any given (u, q, r). It may bepossible for store 220 to include hundreds of thousands of distinctfeatures. In one implementation, some of these features might includeone or more of the following: the country in which user u is located,the language of the country in which user u is located, a cookieidentifier associated with user u, the language of query q, each term inquery q, the time of day user u provided query q, the documents fromrepository r that were presented to user u, each of the terms in thedocuments from repository r that were presented to user u, and/or eachof the terms in the titles of the documents from repository r that werepresented to the user u. Other features might alternatively oradditionally be used.

In another implementation, some of the features might include one ormore of the following in addition to, or instead of, some of thefeatures identified above: the fraction of queries that were provided tothe interface for repository r, the fraction of queries that wereprovided to the interface for repository r versus the interfaces forother repositories, the fraction of queries that contain a term in queryq that were provided to the interface for repository r versus theinterfaces for other repositories, the overall click rate for queriesprovided to the interface for repository r, the click rate for queriesprovided to the interface for repository r for user u, the click ratefor queries provided to the interface of repository r for users in thesame country as user u, and/or the click rate for query q provided tothe interface of repository r.

In a further implementation, the following two features might also beincluded: the click rate of query q provided to the interface ofrepository r for user u, and the fraction of queries q that wereprovided to the interface of repository r for user u. Instead ofdetermining these features directly, models might be generated topredict these features using conventional techniques and the output ofthe models may be used as features.

A model may be created based on this data. In one implementation, themodel may be used to predict, given a new (u, q, r), whether user udesires information from repository r if user u provided query q. Aswill be described in more detail below, the output of the model may beused to determine whether to search a repository, whether to includesearch results from a repository in a search result document, and/or themanner for presenting search results within the search result document.

FIG. 4 is a flowchart of exemplary processing for generating a modelaccording to an implementation consistent with the principles of theinvention. This processing may be performed by a single device 210 or acombination of multiple devices 210.

To facilitate generation of the model, the log data in store 220 may berepresented as sets of instances (block 410). For example, informationmay be identified relating to prior searches by users, such asinformation regarding the users, the queries the users provided, and therepositories from which the search results were obtained and/orselected. This information may be formed into triples (u, q, r), asdescribed above.

A label for each instance may then be determined (block 420). Forexample, it may be determined for each triple (u, q, r) whether user udesired information (e.g., selected a document) in repository r whenuser u provided query q. The labels may be associated with theircorresponding instances in store 220. The features relating to each ofthe instances may also be determined (block 430).

A model may then be generated based on the instances, labels, andfeatures (block 440). For example, a standard machine learning orstatistical technique may be used to determine the probability that useru desires information from repository r when user u provides query q:P(desire|u,q, show_r),where “show_r” indicates that documents from repository r are provided.Any of several well known techniques may be used to generate the model,such as logic regression, boosted decision trees, random forests,support vector machines, perceptrons, and winnow learners. Instead ofgenerating a probability, the model may output a value that reflects aconfidence that user u desires information from repository r when user uprovides query q. The output of the model will be generally referred tohereinafter as a “score,” which may include a probability output and/oran output value.

As explained below, the output of the model may be used to determinewhether to search a repository, whether to include search results from arepository in a search result document, and/or the manner for presentingsearch results within the search result document.

Exemplary Information Retrieval Network

FIG. 5 is an exemplary diagram of a network 500 in which systems andmethods consistent with the principles of the invention may beimplemented. Network 500 may include multiple clients 510 connected tomultiple servers 520-540 via a network 550. Two clients 510 and threeservers 520-540 have been illustrated as connected to network 550 forsimplicity. In practice, there may be more or fewer clients and servers.Also, in some instances, a client may perform a function of a server anda server may perform a function of a client.

Clients 510 may include client entities. An entity may be defined as adevice, such as a personal computer, a wireless telephone, a personaldigital assistant (PDA), a lap top, or another type of computation orcommunication device, a thread or process running on one of thesedevices, and/or an object executable by one of these devices. Servers520-540 may include server entities that gather, process, search, and/ormaintain documents in a manner consistent with the principles of theinvention.

In an implementation consistent with the principles of the invention,server 520 may include a search engine system 525 usable by clients 510.Search engine system 525 may be associated with a number of repositoriesof documents (not shown), such as a web page repository, a newsrepository, an image repository, a products repository, a usenetrepository, a yellow pages repository, a scanned books repository,and/or other types of repositories. These repositories may physicallyreside in one or more memory devices located within server 520 orexternal to server 520. Servers 530 and 540 may store or maintaindocuments that may be associated with one or more of the repositories.

While servers 520-540 are shown as separate entities, it may be possiblefor one or more of servers 520-540 to perform one or more of thefunctions of another one or more of servers 520-540. For example, it maybe possible that two or more of servers 520-540 are implemented as asingle server. It may also be possible for a single one of servers520-540 to be implemented as two or more separate (and possiblydistributed) devices.

Network 550 may include a local area network (LAN), a wide area network(WAN), a telephone network, such as the Public Switched TelephoneNetwork (PSTN), an intranet, the Internet, or a combination of networks.Clients 510 and servers 520-540 may connect to network 550 via wired,wireless, and/or optical connections.

Exemplary Process for Providing Search Results

FIG. 6 is a flowchart of exemplary processing for providing searchresults according to an implementation consistent with the principles ofthe invention. Processing may begin with the receipt of a search query(block 610). For example, a user may access a search engine interfaceusing web browser software on a client, such as client 510 (FIG. 5). Theuser may provide the search query to the search engine interface.

Information concerning the user may be obtained (block 620). Forexample, the user may be identified using, for example, an IP address,cookie information, languages, and/or geographical informationassociated with the user. Conventional techniques may be used forgathering the user information.

In one implementation, a search may be performed on each of therepositories based on the search query (block 630). A set of searchresults may be obtained corresponding to each of the repositories. Anyinformation retrieval technique may be used to identify relevantdocuments to include in the set of search results.

It may then be determined how the search results will be provided basedon the model (block 640). For example, information relating to the user,the search query the user provided, and each of the repositories may beused as inputs to the model. The model may be applied to each repositoryand the output of the model (“score”) may be used to determine whetherto provide search results associated with that repository. It may bedetermined, for example, that search results from the two repositorieswith the highest associated score should be provided. Alternatively, itmay be determined that search results from a particular one of therepositories should always be provided and search results from anotherone or more repositories should also be provided if the score associatedwith the other one or more repositories is greater than the scoreassociated with the particular repository. Alternatively, it may bedetermined that search results from repositories with associated scoresabove a certain threshold should be provided, and if none of the scoresis above the threshold, then provide search results from the repositorywith the highest associated score. Yet other rules for determiningwhether to provide search results associated with a repository mayalternatively or additionally be used.

The output of the model may alternatively, or additionally, be used todetermine the manner in which the search results from the differentrepositories are provided. For example, it may be determined that if thescore associated with a repository is below some threshold, the searchresults associated with the repository may be presented toward thebottom of the search result document presented to the user rather thantoward the top of the search result document. Alternatively, oradditionally, it may be determined that if the score associated with arepository is below some threshold, a link to the search resultsassociated with the repository is presented instead of the searchresults themselves. Yet other rules for determining the manner forproviding search results associated with a repository may alternativelyor additionally be used.

The search results may then be arranged within a search result documentand presented to the user. Each search result may include, for example,a link to a document from the corresponding repository and possibly abrief description of or excerpt from the document.

In another implementation, the repository, or repositories, to searchmay be identified based on the model (block 650). For example,information relating to the user, the search query the user provided,and each of the repositories may be used as inputs to the model. Themodel may be applied to each repository and the output of the model(“score”) may be used to determine which repository to search. It may bedetermined, for example, that the two repositories with the highestassociated score should be searched. Alternatively, it may be determinedthat a particular one of the repositories should always be searched andanother one or more repositories should also be searched if the scoreassociated with the other one or more repositories is greater than thescore associated with the particular repository. Alternatively, it maybe determined that repositories with associated scores above a certainthreshold should be searched, and if none of the scores is above thethreshold, then search the repository with the highest associated score.Yet other rules for determining which repository to search mayalternatively or additionally be used.

A search may be performed to obtain a set of search results from each ofthe identified repositories (block 660). Any conventional informationretrieval technique may be used to identify relevant documents toinclude in the set of search results.

The search results may then be provided based on the model (block 670).For example, the output of the model may be used to determine the mannerin which the search results from different repositories are provided.For example, it may be determined that if the score associated with arepository is below some threshold, the search results associated withthe repository may be presented toward the bottom of the search resultdocument presented to the user rather than toward the top of the searchresult document. Alternatively, or additionally, it may be determinedthat if the score associated with a repository is below some threshold,a link to the search results associated with the repository is presentedinstead of the search results themselves. Other rules for determiningthe manner for providing search results associated with a repository mayalternatively or additionally be used.

The search results may then be arranged within a search result documentand presented to the user. Each search result may include, for example,a link to a document from the corresponding repository and possibly abrief description of or excerpt from the document.

In another implementation, two or more models may be used. For example,a first model may be used to determine whether to search a repository; asecond model may be used to determine whether to include search resultsfrom one of the searched repositories in a search result document; andthe second model, or possibly a third model, may be used to determinethe manner for presenting search results within the search resultdocument. The first, second, and/or third models may be generated basedon one or more factors that differ from each other. For example, in oneimplementation, the output of the first model may be used as an input tothe second model and/or the output of the first and/or second model maybe used as an input to the third model.

It may be possible to provide information concerning this search as logdata to store 220. For example, the information may be used as trainingdata for training or refining the model.

Example

FIGS. 7-10 are diagrams of exemplary implementations consistent with theprinciples of the invention. As shown in FIG. 7, assume that a searchengine system 710 has three associated repositories, including web pagerepository 720, image repository 730, and news repository 740. Web pagerepository 720 may store information relating to web pages. Imagerepository 730 may store information relating to images. News repository740 may store information relating to news documents. Search enginesystem 710 may receive a search query from a user and provide relevantsearch results from one or more of repositories 720-740.

As shown in FIG. 8, assume that a user accesses an interface associatedwith search engine system 710. The interface may be associated with oneof the repositories or none of the repositories. As shown in FIG. 8,assume that the user provides the search query “sunset” to search enginesystem 710. In addition to the search query, search engine system 710may obtain information regarding the user, such as an IP address, cookieinformation, languages, and/or geographical information associated withthe user.

In one implementation, as described above, search engine system 710 mayperform a search on each of repositories 720-740 to obtain a set ofsearch results for each of repositories 720-740. Assume that searchengine system 710 identifies 10 web page results from web pagerepository 720, 10 image results from image repository 730, and 10 newsdocument results from news repository 740 as relevant search results forthe search query “sunset.”

Search engine system 710 may input information relating to the user, thesearch query the user provided, and each of repositories 720-740 asinputs to the model. The model may be used to determine the probabilityof the user desiring information from each of repositories 720-740 whenthe user provides the search query “sunset.”

Assume, for example, that the following outputs are generated by themodel:P(desire|u,q, show_web page repository)=0.45P(desire|u,q, show_image repository)=0.91P(desire|u,q, show_news repository)=0.23,where “u” refers to user information corresponding to the user thatprovided the search query, “q” refers to information corresponding tothe search query the user provided (i.e., “sunset”), and “show_xrepository” (where x corresponds to “web page,” “image,” or “news”)refers to information corresponding to the identified repository. Inthis case, the probability of the user desiring information from webpage repository 720 when the user provides the search query “sunset” is45%; the probability of the user desiring information from imagerepository 730 when the user provides the search query “sunset” is 91%;and the probability of the user desiring information from newsrepository 740 when the user provides the search query “sunset” is 23%.

Search engine system 710 may then use the output of the model withregard to each of repositories 720-740 to determine whether to providesearch results associated with that repository. For example, assume thata rule indicates that search engine system 710 is to provide searchresults only from the repository with the highest score. In this case,search engine system 710 may form a search result document based on the10 image results identified from image repository 730 (i.e., therepository with the highest score—0.91), as shown in FIG. 9.

Alternatively, assume that a rule indicates that search engine system710 is to always provide search results from web page repository 720and, if another repository has an associated score higher than the scoreassociated with web page repository 720, provide search results fromthat repository (or repositories). In this case, search engine system710 may determine that it is to provide search results from both webpage repository 720 and image repository 730 because the scoreassociated with image repository 730 (0.91) is greater than the scoreassociated with web page repository 720 (0.45).

Search engine system 710 may then form a search result document based onthe 10 web page results from web page repository 720 and the 10 imageresults from image repository 730, as shown in FIG. 10. Because thescore associated with image repository 730 is higher than the scoreassociated with web page repository 720 (or some degree higher or higherand greater than a threshold), information regarding the 10 imageresults may be presented in a more prominent location than the 10 webpage results within the search result document, as also shown in FIG.10. The user might select the link associated with the 10 image results(e.g., “SEE 10 IMAGE RESULTS FOR SUNSET>>”) to be presented withadditional information regarding the image results, similar to thatshown in FIG. 9.

Conclusion

Implementations consistent with the principles of the invention maygenerate a model that may be used to predict which repository, orrepositories, a user is likely interested in when the user provides asearch query, and use this model to provide relevant search results tothe user.

The foregoing description of preferred embodiments of the inventionprovides illustration and description, but is not intended to beexhaustive or to limit the invention to the precise form disclosed.Modifications and variations are possible in light of the aboveteachings or may be acquired from practice of the invention.

For example, while series of acts have been described with regard toFIGS. 4 and 6, the order of the acts may be modified in otherimplementations consistent with the principles of the invention.Further, non-dependent acts may be performed in parallel.

Also, exemplary user interfaces have been described with respect toFIGS. 8-10. In other implementations consistent with the principles ofthe invention, the user interfaces may include more, fewer, or differentpieces of information.

The preceding description refers to a user. A “user” is intended torefer to a client, such as a client 510 (FIG. 5), or an operator of aclient.

Further, it has been described that the output of the model (“score”)can be used to determine whether to search a repository, whether toinclude search results from a repository in a search result document,and/or the manner for presenting search results within the search resultdocument. In another implementation, the score may be used as one input,of multiple inputs, to a function that determines whether to search arepository, whether to include search results from a repository in asearch result document, and/or the manner for presenting search resultswithin the search result document.

Further, some of the features described above are more computationallyexpensive to determine than others. For example, features based on thedocuments in the repositories may require those repositories to bequeried and the documents to be fetched. For computational efficiency,an approximate main model may be created based on less computationallyexpensive (e.g., cheaper) features and this approximate main model maybe used to determine which repositories to search. Once the documentsfrom these repositories have been fetched, the full main model may beused to determine from which repositories to provide search results.

Also, it may be possible to use the model according to an “exploration”policy in order to gather information on different repositories. Forexample, it may be desirable to provide search results relating to asub-optimal repository (e.g., presenting news documents rather thanimages). One exploration policy may indicate that documents from arandom repository be presented to a small fraction of users. Anotherexploration policy may indicate that documents from a repository bepresented in proportion to the score (e.g., if the score for images isdetermined to be twice the score for news articles, then images may bepresented twice as often as news articles).

It has been described that a model may be generated to identify arepository (or a set of repositories) based on a likelihood that a userdesires information from the identified repository. In oneimplementation, the model may be constructed as a lookup table with akey determined based on one or more features, such as one or morefeatures relating to the query (e.g., the query terms). The output ofthe lookup table might include a click-through rate (or estimatedclick-through rate) for each of the repositories. In this case, thelikelihood that the user desires information from one of therepositories may be a function of the click-through rate for thatrepository. For example, it might be determined whether to search arepository, whether to include search results from a repository in asearch result document, and/or the manner for presenting search resultsbased on the click-through rates for the repositories.

It will be apparent to one of ordinary skill in the art that aspects ofthe invention, as described above, may be implemented in many differentforms of software, firmware, and hardware in the implementationsillustrated in the figures. The actual software code or specializedcontrol hardware used to implement aspects consistent with theprinciples of the invention is not limiting of the invention. Thus, theoperation and behavior of the aspects were described without referenceto the specific software code—it being understood that one of ordinaryskill in the art would be able to design software and control hardwareto implement the aspects based on the description herein.

No element, act, or instruction used in the present application shouldbe construed as critical or essential to the invention unless explicitlydescribed as such. Also, as used herein, the article “a” is intended toinclude one or more items. Where only one item is intended, the term“one” or similar language is used. Further, the phrase “based on” isintended to mean “based, at least in part, on” unless explicitly statedotherwise.

What is claimed is:
 1. A method comprising: receiving, by one or moreprocessors, a search query; receiving, by the one or more processors,additional information; identifying, by the one or more processors, aplurality of repositories, where each repository includes a differenttype of data of a plurality of types of data; applying, by the one ormore processors and based on the search query and the additionalinformation, a model to each repository of the plurality ofrepositories; receiving, by the one or more processors, a score for eachrepository, wherein receiving the score for each repository is based onapplying the model to each repository based on the search query and theadditional information; searching, by the one or more processors andbased on the score for each repository, at least one of the plurality ofrepositories, based on the search query; and providing, by the one ormore processors, information from more than one of the plurality ofrepositories, providing the information comprising: providing firstinformation from a first repository of the more than one of theplurality of repositories, the first information being associated withsearch results of a first type; and providing second information from asecond repository of the more than one of the plurality of repositories,the second information being associated with a link to search results ofa second type.
 2. The method of claim 1, further comprising: selectingthe at least one of the plurality of repositories based on the scoresatisfying a threshold, where searching the at least one of theplurality of repositories includes: searching the selected at least oneof the plurality of repositories.
 3. The method of claim 1, where theadditional information includes at least one of: Internet Protocol (IP)address information, cookie information, language information, orgeographical information.
 4. The method of claim 1, where theinformation is provided in a search results document, and the methodincludes: positioning, in the search results document, the informationbased on a respective score for each repository of the more than one ofthe plurality of repositories.
 5. The method of claim 1, furthercomprising: generating the model based on information associated withlog data, the information associated with the log data being formed intriples.
 6. The method of claim 1, where the information is provided ina search results document and wherein providing the second informationincludes providing the second information in the search results documentinstead of the search results of the second type.
 7. The method of claim1, where the information is provided in a search results document, andthe method includes: positioning, in the search results document, thefirst information above the second information; wherein positioning thefirst information above the second information is based on the score forthe first repository and the score for the second repository.
 8. Adevice comprising: a memory to store instructions; and a processor toexecute the instructions to: receive a search query; receive additionalinformation; identify a plurality of repositories, where each repositoryincludes a different type of data of a plurality of types of data;apply, based on the search query and the additional information, a modelto each repository of the plurality of repositories; receive a score foreach repository, wherein receiving the score for each repository isbased on applying the model to each repository based on the search queryand the additional information; search, based on the score for eachrepository, at least one of the plurality of repositories based on thesearch query; and provide information from more than one of theplurality of repositories, wherein the processor, in providing theinformation, is to: provide first information from a first repository ofthe more than one of the plurality of repositories, the firstinformation being associated with search results of a first type; andprovide second information from a second repository of the more than oneof the plurality of repositories, the second information beingassociated with a link to search results of a second type.
 9. The deviceof claim 8, where the processor is further to: select the at least oneof the plurality of repositories based on the score satisfying athreshold, where the processor, when searching the at least one of theplurality of repositories, is to: search the selected at least one ofthe plurality of repositories.
 10. The device of claim 8, where theadditional information includes at least one of: Internet Protocol (IP)address information, cookie information, language information, orgeographical information.
 11. The device of claim 8, where theinformation is provided in a search results document, and the processoris further to: position, in the search results document, the informationbased on a respective score for each repository of the more than one ofthe plurality of repositories.
 12. The device of claim 8, the processoris further to: generate the model based on information associated withlog data, the information associated with the log data being formed intriples.
 13. A non-transitory computer-readable medium storinginstructions, the instructions comprising: one or more instructionswhich, when executed by at least one processor, cause the at least oneprocessor to: receive a search query; receive additional information;identify a plurality of repositories, where each repository includes adifferent type of data of a plurality of types of data; apply, based onthe search query and the additional information, a model to eachrepository of the plurality of repositories; receive a score for eachrepository, wherein receiving the score for each repository is based onapplying the model to each repository based on the search query and theadditional information; search, based on the score for each repository,the at least one of the plurality of repositories based on the searchquery; and provide information from more than one of the plurality ofrepositories, wherein the one or more instructions to provide theinformation include one or more instructions to: provide firstinformation from a first repository of the more than one of theplurality of repositories, the first information being associated withsearch results of a first type; and provide second information from asecond repository of the more than one of the plurality of repositories,the second information being associated with a link to search results ofa second type.
 14. The non-transitory computer-readable medium of claim13, where the instructions further include: one or more instructions toselect the at least one of the plurality of repositories based on thescore satisfying a threshold, where the one or more instructions tosearch the at least one of the plurality of repositories include: one ormore instructions to search the selected at least one of the pluralityof repositories.
 15. The non-transitory computer-readable medium ofclaim 13, where the information is provided in a search resultsdocument, and the instructions further include: one or more instructionsto position, in the search results document, the information based on arespective score for each repository of the more than one of theplurality of repositories.
 16. The non-transitory computer-readablemedium of claim 13, where the instructions further include: one or moreinstructions to generate the model based on information associated withlog data, the information associated with the log data being formed intriples.